Molecular Characterization Analysis of Prevalent Infectious Bronchitis Virus and Pathogenicity Assessment of Recombination Strain in China.

Avian coronavirus infectious bronchitis virus (IBV) is a respiratory pathogen of chickens, resulting in severe economic losses in the poultry industry. This study aimed to monitor and isolate the molecular identity of IBV in broiler flocks with respiratory symptoms in eight provinces of China. In total, 910 samples (oropharyngeal and cloacal mixed swabs) from broiler flocks showed IBV positive rates of 17.6% (160/910) using PCR assay. Phylogenetic analysis of the complete S1 genes of 160 IBV isolates was performed and revealed that QX-type (GI-19), TW-type (GI-7), 4/91-type (GI-13), HN08-type (GI-22),TC07-2-type (GVI-1), and LDT3-type (GI-28) exhibited IBV positive rates of 58.15, 25, 8.12, 1.86, 5.62, and 1.25%. In addition, recombination analyses revealed that the four newly IBV isolates presented different recombination patterns. The CK/CH/JS/YC10-3 isolate likely originated from recombination events between strain YX10 (QX-type) and strain TW2575-98 (TW-type), the pathogenicity of which was assessed, comparing it with strain GZ14 (TW-type) and strain CK/CH/GD/JR07-7 (QX-type). The complete S1 gene data from these isolates indicate that IBV has consistently evolved through genetic recombination or mutation, more likely changing the viral pathogenicity and leading to larger outbreaks in chick populations, in China.

The diagnostic value of chest X-ray in coronavirus disease 2019: A comparative study of X-ray and CT.

As the coronavirus disease 2019 (COVID-19) epidemic spreads around the world, the demand for imaging examinations increases accordingly. The value of conventional chest radiography (CCR) remains unclear. In this study, we aimed to investigate the diagnostic value of CCR in the detection of COVID-19 through a comparative analysis of CCR and CT. This study included 49 patients with 52 CT images and chest radiographs of pathogen-confirmed COVID-19 cases and COVID-19-suspected cases that were found to be negative (non-COVID-19). The performance of CCR in detecting COVID-19 was compared to CT imaging. The major signatures that allowed for differentiation between COVID-19 and non-COVID-19 cases were also evaluated. Approximately 75% (39/52) of images had positive findings on the chest x-ray examinations, while 80.7% (42/52) had positive chest CT scans. The COVID-19 group accounted for 88.4% (23/26) of positive chest X-ray examinations and 96.1% (25/26) of positive chest CT scans. The sensitivity, specificity, and accuracy of CCR for abnormal shadows were 88%, 80%, and 87%, respectively, for all patients. For the COVID-19 group, the accuracy of CCR was 92%. The primary signature on CCR was flocculent shadows in both groups. The shadows were primarily in the bi-pulmonary, which was significantly different from non-COVID-19 patients (p = 0.008). The major CT finding of COVID-19 patients was ground-glass opacities in both lungs, while in non-COVID-19 patients, consolidations combined with ground-glass opacities were more common in one lung than both lungs (p = 0.0001). CCR showed excellent performance in detecting abnormal shadows in patients with confirmed COVID-19. However, it has limited value in differentiating COVID-19 patients from non-COVID-19 patients. Through the typical epidemiological history, laboratory examinations, and clinical symptoms, combined with the distributive characteristics of shadows, CCR may be useful to identify patients with possible COVID-19. This will allow for the rapid identification and quarantine of patients.

An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19.

This study established an interpretable machine learning model to predict the severity of coronavirus disease 2019 (COVID-19) and output the most crucial deterioration factors. Clinical information, laboratory tests, and chest computed tomography (CT) scans at admission were collected. Two experienced radiologists reviewed the scans for the patterns, distribution, and CT scores of lung abnormalities. Six machine learning models were established to predict the severity of COVID-19. After parameter tuning and performance comparison, the optimal model was explained using Shapley Additive explanations to output the crucial factors. This study enrolled and classified 198 patients into mild (n = 162; 46.93 ± 14.49 years old) and severe (n = 36; 60.97 ± 15.91 years old) groups. The severe group had a higher temperature (37.42 ± 0.99°C vs. 36.75 ± 0.66°C), CT score at admission, neutrophil count, and neutrophil-to-lymphocyte ratio than the mild group. The XGBoost model ranked first among all models, with an AUC, sensitivity, and specificity of 0.924, 90.91%, and 97.96%, respectively. The early stage of chest CT, total CT score of the percentage of lung involvement, and age were the top three contributors to the prediction of the deterioration of XGBoost. A higher total score on chest CT had a more significant impact on the prediction. In conclusion, the XGBoost model to predict the severity of COVID-19 achieved excellent performance and output the essential factors in the deterioration process, which may help with early clinical intervention, improve prognosis, and reduce mortality.